Diffusion modeling from a Bayesian inference perspective: instead of denoising a sample, we infer it from repeated noisy measurements. Our perspective generalizes Bayesian Flow Networks and achieves better sample quality in fewer steps than BFNs. arxiv.org/abs/2502.07580 github.com/martenlienen...
We are looking for PhD students to start as soon as possible. Application deadline is August 10. Please apply, please share 🙂
ExplainableML@eml-munich.bsky.social · 3mo ago📢 Fully funded PhD @ Helmholtz Munich + TUM We're hiring PhD students to build trustworthy AI systems for science: explainable AI, VLMs, reliable adaptation, agentic workflows. ⏰ Priority deadline 10 Aug 2026. Application details below 👇
📢 Fully funded PhD @ Helmholtz Munich + TUM We're hiring PhD students to build trustworthy AI systems for science: explainable AI, VLMs, reliable adaptation, agentic workflows. ⏰ Priority deadline 10 Aug 2026. Application details below 👇
👋🇧🇷 If you are at #ICLR2026 today, you should talk to @antonbaumann.bsky.social who is presenting our paper about turning pre-trained VLMs into probabilistic models without retraining or fine-tuning. Poster Session 3 ⌚: 10:30am - 1:00pm (local time) 📍: Pavilion 3 P3 - #313 @iclr-conf.bsky.social
We introduce BayesVLM, a training-free post-hoc Bayesian method for uncertainty estimation in pretrained VLMs. BayesVLM yields interpretable, well-calibrated uncertainty with virtually no inference overhead.
SDPO enables RL agents to learn from rich feedback (i.e., not only whether an attempt failed, but why it failed, such as error messages). Even without such rich feedback, SDPO can reflect on past attempts and outperform GRPO. SDPO also accelerates solution discovery at test time!
Jonas Hübotter@jonhue.bsky.social · 9mo agoTraining LLMs with verifiable rewards uses 1bit signal per generated response. This hides why the model failed. Today, we introduce a simple algorithm that enables the model to learn from any rich feedback! And then turns it into dense supervision. (1/n)
Training LLMs with verifiable rewards uses 1bit signal per generated response. This hides why the model failed. Today, we introduce a simple algorithm that enables the model to learn from any rich feedback! And then turns it into dense supervision. (1/n)
This has now been accepted at @iclr-conf.bsky.social !
Martin Trapp@trappmartin.eurosky.social · last yr.Unfortunately, our submission to #NeurIPS didn’t go through with (5,4,4,3). But because I think it’s an excellent paper, I decided to share it anyway. We show how to efficiently apply Bayesian learning in VLMs, improve calibration, and do active learning. Cool stuff! 📝 arxiv.org/abs/2412.06014
It's really hard to tell nowadays what is a made-up joke and what is reality.
David Ho@davidho.bsky.social · 9mo agoI feel like I’ve lost my mind
The Nobel Prize committee should announce the World Cup winner tomorrow
I am hiring a PhD & postdoc to work together with me at KTH on probabilistic machine learning. Both positions are fully funded and part of WASP. I will be attending @euripsconf.bsky.social, if you are around and want to talk about the positions or what we do at KTH, then ping me and we can meet.
Want to work on Trustworthy AI? 🚀 I'm seeking exceptional candidates to apply for the Digital Futures Postdoctoral Fellowship to work with me on Uncertainty Quantification, Bayesian Deep Learning, and Reliability of ML Systems. The position will be co-advised by Hossein Azizpour or Henrik Boström.
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Martin Trapp - Assistant Professor in Machine Learning at KTH Royal Institute of Technology.
trappmartin.github.io
Unfortunately, our submission to #NeurIPS didn’t go through with (5,4,4,3). But because I think it’s an excellent paper, I decided to share it anyway. We show how to efficiently apply Bayesian learning in VLMs, improve calibration, and do active learning. Cool stuff! 📝 arxiv.org/abs/2412.06014
Post-hoc Probabilistic Vision-Language Models
Vision-language models (VLMs), such as CLIP and SigLIP, have found remarkable success in classification, retrieval, and generative tasks. For this, VLMs deterministically map images and text descripti...
arxiv.org
I'm very excited to share notes on Probabilistic AI that I have been writing with @arkrause.bsky.social 🥳 arxiv.org/pdf/2502.05244 These notes aim to give a graduate-level introduction to probabilistic ML + sequential decision-making. I'm super glad to be able to share them with all of you now!
Tomorrow I’ll be presenting our recent work on improving LLMs via local transductive learning in the FITML workshop at NeurIPS. Join us for our ✨oral✨ at 10:30am in east exhibition hall A. Joint work with my fantastic collaborators Sascha Bongni, @idoh.bsky.social, @arkrause.bsky.social
I will present ✌️ BDU workshop papers @ NeurIPS: one by Rui Li (looking for internships) and one by Anton Baumann. 🔗 to extended versions: 1. 🙋 "How can we make predictions in BDL efficiently?" 👉 arxiv.org/abs/2411.18425 2. 🙋 "How can we do prob. active learning in VLMs" 👉 arxiv.org/abs/2412.06014
Post-hoc Probabilistic Vision-Language Models
Vision-language models (VLMs), such as CLIP and SigLIP, have found remarkable success in classification, retrieval, and generative tasks. For this, VLMs deterministically map images and text descripti...
arxiv.org
Martin Trapp@trappmartin.eurosky.social · 2y agoOn my way to #NeurIPS. Looking forward to seeing many friends again. Ping me if you want to chat, always happy to meet new people. :)
